andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2016 knowledge-graph by maker-knowledge-mining
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Introduction: Katie Kent writes: I’m with Zipfian Academy – we’re launching next week as the first 12-week immersive program to teach data science. The program combines the hard and soft skills of data science with introductions to the data science community out here in San Francisco. The launch will be covered by a couple big tech blogs, but we’d love to offer the opportunity to blog about it to some smaller and well-respected data science blogs like yours. I don’t know anything about this but I took a look at the website and it looks pretty cool. Maybe in a future iteration of their course, they can teach Stan, once it has a few more useful features such as VB and EP.
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2 The program combines the hard and soft skills of data science with introductions to the data science community out here in San Francisco. [sent-2, score-1.581]
3 The launch will be covered by a couple big tech blogs, but we’d love to offer the opportunity to blog about it to some smaller and well-respected data science blogs like yours. [sent-3, score-1.864]
4 I don’t know anything about this but I took a look at the website and it looks pretty cool. [sent-4, score-0.557]
5 Maybe in a future iteration of their course, they can teach Stan, once it has a few more useful features such as VB and EP. [sent-5, score-0.797]
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same-blog 1 1.0000001 2016 andrew gelman stats-2013-09-11-Zipfian Academy, A School for Data Science
Introduction: Katie Kent writes: I’m with Zipfian Academy – we’re launching next week as the first 12-week immersive program to teach data science. The program combines the hard and soft skills of data science with introductions to the data science community out here in San Francisco. The launch will be covered by a couple big tech blogs, but we’d love to offer the opportunity to blog about it to some smaller and well-respected data science blogs like yours. I don’t know anything about this but I took a look at the website and it looks pretty cool. Maybe in a future iteration of their course, they can teach Stan, once it has a few more useful features such as VB and EP.
Introduction: About 12 years ago Greg Wawro, Sy Spilerman, and I started a M.A. program here in Quantitative Methods in Social Sciences, jointly between the departments of history, economics, political science, sociology, psychology, and statistics. We created a bunch of new features for the program, including an interdisciplinary course based on this book . And here’s their new logo: Don’t blame me for the pie-chart motif! Seriously, though, the program is great. I’m proud to have gotten it started, and I’m impressed by the progress that Chris Weiss and others have made in expanding the program during the past decade.
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Introduction: Frontiers of Science is a course offered as part of Columbia University’s Core Curriculum. The course is controversial, with some people praising its overview of several areas of science, and others feeling that a more traditional set of introductory science courses would do the job better. Last month, the faculty in charge of the course wrote the following public letter : The United States is in the midst of a debate over the value of a traditional college education. Why enroll in a place like Columbia College when you can obtain an undergraduate degree for $10,000 or learn everything from Massive Open Online Courses? In more parochial terms, what is the value added by approaches such as Columbia’s Core Curriculum? Recently students in our Core Course, Frontiers of Science (FoS), provided a partial answer. The FoS faculty designed a survey to gauge the scientific skills and knowledge of the Class of 2016 both before and after taking FoS. In an assembly held during orientati
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Introduction: Rachel Schutt and Cathy O’Neil just came out with a wonderfully readable book on doing data science, based on a course Rachel taught last year at Columbia. Rachel is a former Ph.D. student of mine and so I’m inclined to have a positive view of her work; on the other hand, I did actually look at the book and I did find it readable! What do I claim is the least important part of data science? Here’s what Schutt and O’Neil say regarding the title: “Data science is not just a rebranding of statistics or machine learning but rather a field unto itself.” I agree. There’s so much that goes on with data that is about computing, not statistics. I do think it would be fair to consider statistics (which includes sampling, experimental design, and data collection as well as data analysis (which itself includes model building, visualization, and model checking as well as inference)) as a subset of data science. The question then arises: why do descriptions of data science focus so
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Introduction: Katie Kent writes: I’m with Zipfian Academy – we’re launching next week as the first 12-week immersive program to teach data science. The program combines the hard and soft skills of data science with introductions to the data science community out here in San Francisco. The launch will be covered by a couple big tech blogs, but we’d love to offer the opportunity to blog about it to some smaller and well-respected data science blogs like yours. I don’t know anything about this but I took a look at the website and it looks pretty cool. Maybe in a future iteration of their course, they can teach Stan, once it has a few more useful features such as VB and EP.
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Introduction: Katie Kent writes: I’m with Zipfian Academy – we’re launching next week as the first 12-week immersive program to teach data science. The program combines the hard and soft skills of data science with introductions to the data science community out here in San Francisco. The launch will be covered by a couple big tech blogs, but we’d love to offer the opportunity to blog about it to some smaller and well-respected data science blogs like yours. I don’t know anything about this but I took a look at the website and it looks pretty cool. Maybe in a future iteration of their course, they can teach Stan, once it has a few more useful features such as VB and EP.
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Introduction: Warning – this blog post is meant to encourage some loose, fuzzy and possibly distracting thoughts about the practice of statistics in research endeavours. There maybe spelling and grammatical errors as well as a lack of proper sentence structure. It may not be understandable to many or even possibly any readers. But somewhat more seriously, its better that “ConUnMax” So far I have five maxims 1. Explicit models of uncertanty are useful but – always wrong and can always be made less wrong 2. If the model is formally a probability model – always use probability calculus (Bayes) 3. Always useful to make the model a formal probability model – no matter what (Bayesianisn) 4. Never use a model that is not empirically motivated and strongly empirically testable (Frequentist – of the anti-Bayesian flavour) 5. Quantitative tools are always just a means to grasp and manipulate models – never an end in itself (i.e. don’t obsess over “baby” mathematics) 6. If one really understood st
Introduction: Here’s the link . When you’re on the hangout, please mute your own microphone! I’ll have the computer point at the blackboard. You can follow along with the slides: for the first hour for the second hour P.S. Apparently there is some limit on number of hangout participants (see comments). I didn’t know about that! Maybe next time will try “on air” hangout, I will have to learn more about this. Next week the teaching asst will do the course so no hangout, then in two weeks there is no class because it’s the day after Halloween and that’s a holiday around here. So we’ll resume this on Fri 8 Nov. See you then! P.P.S. Those of you who were able to join the hangout: Could you please let me know how the visual and sound quality were? Thanks.
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